39,431 research outputs found
Chance-Constrained Outage Scheduling using a Machine Learning Proxy
Outage scheduling aims at defining, over a horizon of several months to
years, when different components needing maintenance should be taken out of
operation. Its objective is to minimize operation-cost expectation while
satisfying reliability-related constraints. We propose a distributed
scenario-based chance-constrained optimization formulation for this problem. To
tackle tractability issues arising in large networks, we use machine learning
to build a proxy for predicting outcomes of power system operation processes in
this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains
cheaper and more reliable plans than other candidates
A hierarchical Bayesian approach to record linkage and population size problems
We propose and illustrate a hierarchical Bayesian approach for matching
statistical records observed on different occasions. We show how this model can
be profitably adopted both in record linkage problems and in capture--recapture
setups, where the size of a finite population is the real object of interest.
There are at least two important differences between the proposed model-based
approach and the current practice in record linkage. First, the statistical
model is built up on the actually observed categorical variables and no
reduction (to 0--1 comparisons) of the available information takes place.
Second, the hierarchical structure of the model allows a two-way propagation of
the uncertainty between the parameter estimation step and the matching
procedure so that no plug-in estimates are used and the correct uncertainty is
accounted for both in estimating the population size and in performing the
record linkage. We illustrate and motivate our proposal through a real data
example and simulations.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS447 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean
The purpose of this paper is to provide a hierarchical dynamic mission
planning framework for a single autonomous underwater vehicle (AUV) to
accomplish task-assign process in a limited time interval while operating in an
uncertain undersea environment, where spatio-temporal variability of the
operating field is taken into account. To this end, a high level reactive
mission planner and a low level motion planning system are constructed. The
high level system is responsible for task priority assignment and guiding the
vehicle toward a target of interest considering on-time termination of the
mission. The lower layer is in charge of generating optimal trajectories based
on sequence of tasks and dynamicity of operating terrain. The mission planner
is able to reactively re-arrange the tasks based on mission/terrain updates
while the low level planner is capable of coping unexpected changes of the
terrain by correcting the old path and re-generating a new trajectory. As a
result, the vehicle is able to undertake the maximum number of tasks with
certain degree of maneuverability having situational awareness of the operating
field. The computational engine of the mentioned framework is based on the
biogeography based optimization (BBO) algorithm that is capable of providing
efficient solutions. To evaluate the performance of the proposed framework,
firstly, a realistic model of undersea environment is provided based on
realistic map data, and then several scenarios, treated as real experiments,
are designed through the simulation study. Additionally, to show the robustness
and reliability of the framework, Monte-Carlo simulation is carried out and
statistical analysis is performed. The results of simulations indicate the
significant potential of the two-level hierarchical mission planning system in
mission success and its applicability for real-time implementation
OCL Plus:Processes and Events in Object-Centred Planning
An important area in AI Planning is the expressiveness of planning domain
specification languages such as PDDL, and their aptitude for modelling real
applications. This paper presents OCLplus, an extension of a hierarchical object
centred planning domain definition language, intended to support the representation
of domains with continuous change. The main extension in OCLplus provides
the capability of interconnection between the planners and the changes that are
caused by other objects of the world. To this extent, the concept of event and process
are introduced in the Hierarchical Task Network (HTN), object centred planning
framework in which a process is responsible for either continuous or discrete
changes, and an event is triggered if its precondition is met. We evaluate the use of
OCLplus and compare it with a similar language, PDDL+
A Distributed Model Predictive Control Framework for Road-Following Formation Control of Car-like Vehicles (Extended Version)
This work presents a novel framework for the formation control of multiple
autonomous ground vehicles in an on-road environment. Unique challenges of this
problem lie in 1) the design of collision avoidance strategies with obstacles
and with other vehicles in a highly structured environment, 2) dynamic
reconfiguration of the formation to handle different task specifications. In
this paper, we design a local MPC-based tracking controller for each individual
vehicle to follow a reference trajectory while satisfying various constraints
(kinematics and dynamics, collision avoidance, \textit{etc.}). The reference
trajectory of a vehicle is computed from its leader's trajectory, based on a
pre-defined formation tree. We use logic rules to organize the collision
avoidance behaviors of member vehicles. Moreover, we propose a methodology to
safely reconfigure the formation on-the-fly. The proposed framework has been
validated using high-fidelity simulations.Comment: Extended version of the conference paper submission on ICARCV'1
Simulation-based reachability analysis for nonlinear systems using componentwise contraction properties
A shortcoming of existing reachability approaches for nonlinear systems is
the poor scalability with the number of continuous state variables. To mitigate
this problem we present a simulation-based approach where we first sample a
number of trajectories of the system and next establish bounds on the
convergence or divergence between the samples and neighboring trajectories. We
compute these bounds using contraction theory and reduce the conservatism by
partitioning the state vector into several components and analyzing contraction
properties separately in each direction. Among other benefits this allows us to
analyze the effect of constant but uncertain parameters by treating them as
state variables and partitioning them into a separate direction. We next
present a numerical procedure to search for weighted norms that yield a
prescribed contraction rate, which can be incorporated in the reachability
algorithm to adjust the weights to minimize the growth of the reachable set
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